Search Results (150)

Rapid urbanization in coastal regions presents complex challenges for environmental management and public safety. Accurate, high-resolution wind field monitoring is critical for urban disaster mitigation, infrastructure resilience, and pollutant dispersion analysis in these densely populated areas. However, utilizing massive multi-source satellite remote sensing data for precise prediction remains difficult due to the spatiotemporal heterogeneity caused by the land–sea interface. To address this, this study proposes a novel lightweight Geospatial Artificial Intelligence (GeoAI) framework (DA-DSC-UNet) designed to predict wind fields in coastal urban environments (e.g., Fujian, China). We constructed a dataset by integrating multi-source satellite scatterometer products (including Advanced Scatterometer (ASCAT), Fengyun-3E (FY-3E), and Quick Scatterometer (QuickSCAT)) and buoy observations. The framework employs a UNet architecture enhanced with dual attention mechanisms (Efficient Channel Attention (ECA) and Convolutional Block Attention Module (CBAM)) to adaptively extract features from remote sensing signals, focusing on critical spatial regions like urban coastlines. Additionally, depthwise separable convolutions (DSCs) are introduced to ensure the model is lightweight and efficient for potential deployment in urban monitoring systems. Results demonstrate that our approach significantly outperforms existing deep learning models (reducing Mean Absolute Error (MAE) by 14–25.8%) and exhibits exceptional robustness against observational noise. This work demonstrates the potential of deep learning in enhancing the value of remote sensing data for urban resilience, sustainable development (SDG 11), and environmental monitoring in complex coastal zones. Full article

Ku-band scatterometers lose extensive Sea Surface Vector Wind (SSVW) observations under extreme winds, heavy precipitation, or instrument anomalies, degrading forecast and assimilation skill. Traditional interpolation fails to reconstruct non-linear wind structures, whereas existing deep learning inpainting is hampered by scarce public datasets, high computational cost and insufficient continuity modeling. We propose WMamba, an Attention-Structured State Space Duality (ASSD)-based framework that exploits wind continuity to encode global dependencies with O(N) complexity for accurate SSVW inpainting. A Grouped Multiscale Attention Block (GMAB) ensures accurate fine-scale wind detail reconstruction by mitigating local pixel degradation. We also introduce L-WMamba, a lightweight 0.36 M-parameter variant suitable for resource-limited devices. Moreover, we release the SSVW Inpainting Dataset (WID), comprising 123,841 high-wind HY-2B HSCAT samples (2018–2022), as an open benchmark. Experiments demonstrate that WMamba outperforms GRL (state-of-the-art) decreasing the RMSE for wind speed and direction by 11.4% and 6.3%, respectively, while achieving a 94.7% reduction in parameters. In particular, WMamba effectively inpaints wind details, as evidenced by the highest MS-SSIM and RAPSD scores. This framework and dataset establish a robust baseline for extreme-weather SSVW recovery. Full article

This study demonstrates the positive impact of scatterometer wind-based bogus vortex initialization on forecasts of Typhoon Doksuri (2023). In this scheme, the NCEP analysis vortex in the initial conditions is replaced with a bogus vortex. A regression model links the scatterometer wind-derived 34 kt wind radius with the radius of maximum sea-level pressure gradient, a required parameter in Fujita’s bogus formula. The cyclonic circulation center identified in the scatterometer wind field is designated as the typhoon center. The resulting bogus vortex provides a more realistic representation of the low-level circulation, center location, and intensity. Numerical experiments with the WRF model, configured with two-way nested domains (9–3 km) and 115 vertical levels below the model top at 1 hPa, show that the scatterometer wind-bogus scheme effectively improves the initial vortex position and minimum sea-level pressure, slightly enhances track forecasts, and substantially improves intensity forecasts, particularly during rapid intensification and weakening stages of Typhoon Doksuri over the western North Pacific. Furthermore, comparisons with Himawari-9 AHI infrared observations indicate that forecasts with bogus vortex initialization better reproduce the eye, eyewall, and spiral rainband structures than forecasts without it. These results underscore the value of scatterometer observations for improving typhoon forecasts. Full article

Ocean surface wind vector (OWV) is a key variable for ocean remote sensing and tropical cyclone (TC) monitoring. This study presents the first comprehensive intercomparison of Ku-band OWV products from FY-3E/WindRAD and HY-2B/SCA scatterometers using full-year data from 2022 (583,805 spatiotemporal collocations), with both sensors sampling the morning–evening local-time sector in sun-synchronous orbits. Results indicate strong agreement in wind speed (R = 0.95; mean bias −0.47 m/s; RMSE 1.30 m/s) and wind direction (mean bias 0.22°; std 28.13°) for wind speeds ≥ 3.4 m/s (Beaufort scale B3 and above), with the highest consistency across Beaufort scale 3–8 (B3–B8); however, at wind speeds greater than 20.8 m/s (B9) the bias increases. A fusion leveraging FY-3E’s fine resolution and HY-2B’s wide coverage is implemented and applied to Super Typhoon Hinnamnor (2022), enhancing the spatial coverage and structural detail of TC winds. Quadrant 34 kt wind radii (R34) are estimated from the fused wind fields and evaluated against the best-track data from the Joint Typhoon Warning Center (JTWC), showing close agreement during compact, symmetric TC stages but larger differences during structural reorganization. Overall, the findings confirm inter-satellite consistency for the two Chinese scatterometers and demonstrate the practical value of a multi-source fusion approach that benefits TC monitoring, wind radii estimation, and marine weather services. Full article

Sea surface temperature (SST) gradients modulate surface wind variability at the mesoscale O(100 km), with relevant impacts on surface fluxes, rainfall, cloudiness and storms. The dependence of the SST-wind coupling mechanisms on physical environmental conditions has been proven using global ERA5 reanalysis data, regional observations and models. However, recent literature calls for the need of an observational confirmation to overcome the limitations of numerical simulations in representing such turbulent processes. Here, we employ O(10 km) MetOp A observations of surface wind and SST to verify the dependence of the downward momentum mixing (DMM) mechanism on large-scale wind U and atmospheric stability. We propose a simple empirical model describing how the coupling intensity varies as a function of U, where we account for the role of the characteristic SST length scale LSST and the boundary layer height h in determining the balance between the advective and response timescales, and therefore the decoupling of the atmospheric response from the SST forcing due to advection. Fitting such a model to the observations, we retrieve a scaling with U that depends on the atmospheric stability, in agreement with the literature. The physical interpretation from ERA5 is confirmed, albeit relevant discrepancies emerge in stable regimes and specific regional contexts. This suggests that global numerical models are not able to properly reproduce the coupling in certain conditions, which might have important implications for air–sea fluxes. Full article

Wind is an important renewable energy source, and even minor variations in wind speed will significantly impact wind power generation. The objective of this study was to systematically assess the impacts of climate change on wind energy resources in the South China Sea (SCS) under future climate projections. To achieve this, we employed a multi-model ensemble approach based on Coupled Model Intercomparison Project Phase 6 (CMIP6) data under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results demonstrated that, in comparison with scatterometer wind data, the CMIP6 historical results (1995–2014) showed good performance in capturing the spatiotemporal distribution of wind power density (WPD) in the SCS. There were regional discrepancies in the central SCS due to the complex monsoon-driven wind dynamics. Future projections revealed an overall increase in annual mean wind power density (WPD) across the entire SCS by the mid-21st century (2046–2065) and late 21st century (2080–2099). The seasonal analyses indicated significant WPD increases in summer, especially in the northern SCS and the region adjacent to the Kalimantan strait. The increase in summer (>40 × 10⁻⁴ m/s/year under SSP5-8.5) is about triple that in winter. In the late 21st century, an increase in WPD exceeding 10% can be generally anticipated under the SSP2-4.5 and SSP5-8.5 scenarios in all seasons. The extreme wind in the northern and central SCS will further increase by 5% under the three scenarios, which will add an extra extreme load to wind turbines and related marine facilities. These assessments are essential for wind farm planning and long-term energy production evaluations in the SCS. Based on the findings in this study, specific areas of concern can be targeted to conduct localized downscaling analyses and risk assessments. Full article

The primary contribution of this study lies in analyzing the dynamic drivers during two anomalous sea level rise events in the Bohai Sea through coupled numeric modeling using the Weather Research and Forecasting (WRF) model and the Finite-Volume Community Ocean Model (FVCOM) integrated with the Simulating Waves Nearshore (SWAN) module (hereafter referred to as FVCOM-SWAVE). WRF-derived wind speeds (0.05° grid resolution) were validated against Haiyang-2 (HY-2) scatterometer observations, yielding a root mean square error (RMSE) of 1.88 m/s and a correlation coefficient (Cor) of 0.85. Similarly, comparisons of significant wave height (SWH) simulated by FVCOM-SWAVE (0.05° triangular mesh) with HY-2 altimeter data showed an RMSE of 0.67 m and a Cor of 0.84. Four FVCOM sensitivity experiments were conducted to assess drivers of sea level rise, validated against tide gauge observations. The results identified tides as the primary driver of sea level rise, with wind stress and elevation forcing (e.g., storm surge) amplifying variability, while currents exhibited negligible influence. During the two events, i.e., 20–21 October and 25–26 August 2024, elevation forcing contributed to localized sea level rises of 0.6 m in the northern and southern Bohai Sea and 1.1 m in the southern Bohai Sea. A 1 m surge in the northern region correlated with intense Yellow Sea winds (20 m/s) and waves (5 m SWH), which drove water masses into the Bohai Sea. Stokes transport (wave-driven circulation) significantly amplified water levels during the 21 October and 26 August peak, underscoring critical wave–tide interactions. This study highlights the necessity of incorporating tides, wind, elevation forcing, and wave effects into coastal hydrodynamic models to improve predictions of extreme sea level rise events. In contrast, the role of imposed boundary current can be marginalized in such scenarios. Full article

This study presents an objective scheme to construct hurricane bogus vortices based on satellite microwave scatterometer observations of sea surface wind vectors. When specifying a bogus vortex using Fujita’s formula, the required parameters include the center position and the radius of the maximum gradient of sea level pressure (R₀). We first propose determining the tropical cyclone (TC) center position as the cyclonic circulation center obtained from sea surface wind observations and then establishing a regression model between R₀ and the radius of 34-kt sea surface wind of scatterometer observations. The radius of 34-kt sea surface wind (R₃₄) is commonly used as a measure of TC size. The center positions determined from HaiYang-2B/2C/2D Scatterometers, MetOp-B/C Advanced Scatterometers, and FengYun-3E Wind Radar compared favorably with the axisymmetric centers of hurricane rain/cloud bands revealed by Advanced Himawari Imager observations of brightness temperature for the western Pacific landfalling typhoons Doksuri, Khanun, and Haikui in 2023. Furthermore, regression equations between R₀ and the scatterometer-determined radius of 34-kt wind are developed for tropical storms and category-1, -2, -3, and higher hurricanes over the Northwest Pacific (2022–2023). The bogus vortices thus constructed are more realistic than those built without satellite sea surface wind observations. Full article

Rain backscatter corrupts Ku-band scatterometer wind retrieval by mixing with the signatures of the ${\sigma }^{\circ }$ (backscatter measurements) on the sea surface. The measurements are sensitive to rain clouds due to the short wavelength, and the rain-contaminated measurements in a wind vector cell (WVC) deviate from the simulated measurements using the wind geophysical model function (GMF). Therefore, quality control (QC) is essential to guarantee the retrieved winds’ quality and consistency. The normalized maximum likelihood estimator (MLE) residual ($R_n$) is a QC indicator representing the distance between the ${\sigma }^{\circ }$ measurements and the wind GMF; it works locally for one WVC. $J_{OSS}$ is another QC indicator. It is the speed component of the observation cost function, which is sensitive to spatial inconsistencies in the wind field. $R_{n}J$ is a combined indicator, and it takes both local information ($R_n$) and spatial consistency ($J_{OSS}$) into account. This paper focuses on the QC for WindRAD, a dual-frequency (C and Ku band) rotating-fan-beam scatterometer. The $R_n$ and $R_{n}J$ have been established and thoroughly investigated for Ku-band-only and combined C–Ku wind retrieval. An additional 0.4% of WVCs are rejected with $R_{n}J$, as compared to $R_n$ for both Ku-band-only and combined C–Ku wind retrievals. The number of accepted WVCs with high rain rates (>7 mm/h) is reduced by half, and the wind verification with respect to ECMWF winds is generally improved. The C-band measurements are little influenced by rain, so the Ku-based $R_n$ is more effective for the combined C–Ku wind retrieval than the total $R_n$ from both the C and Ku bands. The rejection rate of the combined C–Ku retrievals reduces by about half compared to the Ku-band-only retrieval, with similar wind verification statistics. Therefore, adding the C band into the retrieval suppresses the rain effect, and acceptable QC capabilities can be achieved with fewer rejected winds. Full article

Over the past decade, several studies based on coupled ocean–atmosphere simulations have shown that the oceanic surface current feedback to the atmosphere (CFB) leads to a slow-down of the mean oceanic circulation and, overall, to the so-called eddy killing effect, i.e., a sink of kinetic energy from oceanic eddies to the atmosphere that damps the oceanic mesoscale activity by about 30%, with upscaling effects on large-scale currents. Despite significant improvements in the representation of western boundary currents and mesoscale eddies in numerical models, some discrepancies remain when comparing numerical simulations with satellite observations. These discrepancies include a stronger wind and wind stress response to surface currents and a larger air–sea kinetic energy flux from the ocean to the atmosphere in numerical simulations. However, altimetric gridded products are known to largely underestimate mesoscale activity, and the satellite observations operate at different spatial and temporal resolutions and do not simultaneously measure surface currents and wind stress, leading to large uncertainties in air–sea mechanical energy flux estimates. ODYSEA is a new satellite mission project that aims to simultaneously monitor total surface currents and wind stress with a spatial sampling interval of 5 km and 90% daily global coverage. This study evaluates the potential of ODYSEA to measure surface winds, currents, energy fluxes, and ocean–atmosphere coupling coefficients. To this end, we generated synthetic ODYSEA data from a high-resolution coupled ocean–wave–atmosphere simulation of the Gulf Stream using ODYSIM, the Doppler scatterometer simulator for ODYSEA. Our results indicate that ODYSEA would significantly improve the monitoring of eddy kinetic energy, the kinetic energy cascade, and air–sea kinetic energy flux in the Gulf Stream region. Despite the improvement over the current measurements, the estimates of the coupling coefficients between surface currents and wind stress may still have large uncertainties due to the noise inherent in ODYSEA, and also due to measurement capabilities related to wind stress. This study evidences that halving the measurement noise in surface currents would lead to a more accurate estimation of the surface eddy kinetic energy and wind stress coupling coefficients. Since measurement noise in surface currents strongly depends on the square root of the transmit power of the Doppler scatterometer antenna, noise levels can be reduced by increasing the antenna length. However, exploring other alternatives, such as the use of neural networks, could also be a promising approach. Additionally, the combination of wind stress estimation from ODYSEA with other satellite products and numerical simulations could improve the representation of wind stress in gridded products. Future efforts should focus on the assessment of the potential of ODYSEA in quantifying the production of eddy kinetic energy through horizontal energy fluxes and air–sea energy fluxes related to divergent and rotational motions. Full article

A storm surge, classified as an extreme natural disaster, refers to unusual sea level fluctuations induced by severe atmospheric disturbances such as typhoons. Existing reanalysis data, such as ERA5, significantly underestimates the location and maximum wind speed of typhoons. Therefore, this study initially assesses the accuracy of tropical cyclone positions and peak wind speeds in the ERA5 reanalysis dataset. These results are compared against tropical cyclone parameters from the IBTrACS (International Best Track Archive for Climate Stewardship). The position deviation of tropical cyclones in ERA5 is mainly within the range of 10 to 60 km. While the correlation of maximum wind speed is significant, there is still considerable underestimation. A wind field reconstruction model, incorporating tropical cyclone characteristics and a distance correction factor, was employed. This model considers the effects of the surrounding environment during the movement of the tropical cyclone by introducing a decay coefficient. The reconstructed wind field significantly improved the representation of the typhoon eyewall and high-wind-speed regions, showing a closer match with wind speeds observed by the HY-2B scatterometer. Through simulations using the FVCOM (Finite Volume Community Ocean Model) storm surge model, the reconstructed wind field demonstrated higher accuracy in reproducing water level changes at Tanxu, Gaoqiao, and Zhangjiabang stations. During the typhoon’s landfall in Shanghai, the area with the greatest water level increase was primarily located in the coastal waters of Pudong New Area, Shanghai, where the highest total water level reached 5.2 m and the storm surge reached 4 m. The methods and results of this study provide robust technical support and a valuable reference for further storm surge forecasting, marine disaster risk assessment, and coastal disaster prevention and mitigation efforts. Full article

High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers, scatterometers, and synthetic aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10° longitude × 10° latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5 and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15 m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center (5%). Full article

In this study, wind speed sensitivity and calibration bias were first determined for Surface Water and Ocean Topography (SWOT) satellite Ka-band Radar Interferometer (KaRIn) Normalized Radar Backscatter Cross Section (NRCS) data at VV and HH polarizations. Here, the calibration bias was estimated by comparing the KaRIn NRCS with collocated simulations from a model developed using Global Precipitation Measurement (GPM) satellite Dual-frequency Precipitation Radar (DPR) data. To recalibrate the bias, the correlation coefficient between the KaRIn data and the simulations was estimated, and the data with the corresponding top 10% correlation coefficients were used to estimate the recalibration coefficients. After recalibration, a Ka-band NRCS model was developed from the KaRIn data to retrieve ocean surface wind speeds. Finally, wind speed retrievals were evaluated using the collocated European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis winds, Haiyang-2C scatterometer (HY2C-SCAT) winds and National Data Buoy Center (NDBC) and Tropical Atmosphere Ocean (TAO) buoy winds. Evaluation results show that the Root Mean Square Error (RMSE) at both polarizations is less than 1.52 m/s, 1.34 m/s and 1.57 m/s, respectively, when compared to ECMWF, HY2C-SCAT and buoy collocated winds. Moreover, both the bias and RMSE were constant with the incidence angles and polarizations. This indicates that the winds from the SWOT KaRIn data are capable of correcting the sea state bias for sea surface height products. Full article

The presence of melt ponds on the surface of Arctic Sea ice affects its albedo, thermal properties, and overall melting rate; thus, the detection of melt pond onset is of significant importance for understanding the Arctic’s changing climate. This study investigates the utility of a novel method for detecting the onset of melt ponds on sea ice using a satellite-based, dual-sensor C-band approach, whereby Sentinel-1 provides horizontally polarized (HH) data and Advanced SCATterometer (ASCAT) provides vertically polarized (VV) data. The co-polarized ratio (VV/HH) is used to detect the presence of melt ponds on landfast sea ice in the Canadian Arctic Archipelago in 2017 and 2018. ERA-5 air temperature and wind speed re-analysis datasets are used to establish the VV/HH threshold for pond onset detection, which have been further validated by Landsat-8 reflectance. The co-polarized ratio threshold of three standard deviations from the late winter season (April) mean co-pol ratio values are used for assessing pond onset detection associated with the air temperature and wind speed data, along with visual observations from Sentinel-1 and cloud-free Sentinel-2 imagery. In 2017, the pond onset detection rates were 70.59% for FYI and 92.3% for MYI. Results suggest that this method, because of its dual-platform application, has potential for providing large-area coverage estimation of the timing of sea ice melt pond onset using different earth observation satellites. Full article

A new ASCAT coastal wind product based on a 12.5 km grid size is presented. The new product contains winds up to the coast line and is identical to the current operational coastal product over the open ocean. It is based on the assumption that within a wind vector cell land and sea have constant radar cross section. With an accurate land fraction calculated from ASCAT’s spatial response function and a detailed land mask, the land correction can be obtained with a simple linear regression. The coastal winds stretch all the way to the coast, filling the coastal gap in the operational coastal ASCAT product, resulting in three times more winds within a distance of 20 km from the coast. The Quality Control (QC), based on the regression error and the regression bias error, reduces this abundance somewhat. A comparison of wind speed pdfs with those from NWP forecasts shows that the influence of land in the land-corrected scatterometer product appears more reasonable and starts not as far offshore as that in the NWP forecasts. The VRMS difference with moored buoys increases slightly from about 2.4 m/s at 20 km or more from the coast to 4.2 m/s at less than 5 km, where coastal wind effects clearly contribute to the latter difference. While the QC based on the regression bias error flags many WVCs that compare well with buoys, the land-corrected coastal product with more abundant coastal winds appears useful for nowcasting and other coastal wind applications. Full article